ICLR Topic Selection
Use this when a project is still movable. ICLR is broad, but the paper should teach the learning
community something about representations, objectives, models, data, optimization, evaluation, or
deployment.
Strong ICLR signals
- A clear representation-learning, model-behavior, optimization, generative modeling, RL, theory, or
evaluation contribution.
- Evidence that changes how researchers should build, analyze, or judge learning systems.
- A simple central claim that can be verified by focused theory, experiments, or artifacts.
- Interest beyond one dataset, product, or application vertical.
- Honest limitations and ethics treatment for high-impact model or data claims.
Weak ICLR signals
- Pure application paper with little learning insight.
- Incremental benchmark bump without mechanism, analysis, or robust evidence.
- Closed system claim that reviewers cannot inspect or reproduce.
- Dataset-only paper without a learning-representation or evaluation advance.
- Theory result disconnected from modern learning practice and not routed to a theory-focused venue.
Routing logic
- Prefer NeurIPS or ICML for broader ML method/theory work with less ICLR-specific representation
framing.
- Prefer AISTATS or UAI for statistics, uncertainty, causal, or probabilistic emphasis.
- Prefer ACL, CVPR, KDD, or robotics/HCI venues when the contribution is primarily domain-specific.
- Prefer workshops when the idea is timely but under-evidenced.
Fit-versus-route decision table
ICLR's center of gravity is deep representation learning: architectures, self-supervision,
generative models, foundation models, RL with deep function approximation, optimization for deep
nets, interpretability, and alignment. Score the project against that center before routing.
| Project shape |
ICLR fit |
Better route if not ICLR |
| New self-supervised objective with analysis |
Strong |
— |
| Theory explaining a deep-net phenomenon |
Strong |
AISTATS/UAI if purely statistical |
| LLM/foundation-model behavior study |
Strong |
ACL if narrowly language-specific |
| Benchmark bump, no mechanism |
Weak |
Domain venue or workshop |
| Causal/uncertainty emphasis |
Plausible |
AISTATS or UAI |
| Deployed application, little learning insight |
Weak |
KDD, CVPR, robotics/HCI venue |
Worked vignette
A team has a method that improves recommendation click-through in production. As written it is an
application paper. To make it ICLR-shaped, they extract the representation-learning claim: a new
contrastive objective that yields embeddings transferring across catalogs, demonstrated with an
ablation and a probe on a public dataset. The product result becomes one validation point, not the
contribution. If that reframing fails to surface a learning insight, the honest route is KDD.
Reviewer-pushback patterns
- "No learning insight, just engineering." Reframe around the mechanism or route to a domain venue.
- "Dataset-only paper." Add an evaluation or representation advance, or target a datasets-and-
benchmarks track instead.
- "Theory disconnected from practice." Tie the result to an observed deep-learning phenomenon.
Output format
[ICLR fit] strong / plausible / weak / no
[Core learning insight] <one sentence>
[Evidence required] <theory, experiment, benchmark, artifact>
[Best venue route] ICLR / NeurIPS / ICML / AISTATS / UAI / domain venue / workshop
[Reframe] <how to make the paper more ICLR-shaped>
1---2name: iclr-topic-selection3description: Use when deciding whether a project is a strong ICLR submission, should be reframed for ICLR, or should be routed to NeurIPS, ICML, AAAI, AISTATS, ACL, CVPR, KDD, or another venue. Use when a project lacks a clear representation-learning insight, when an application result needs a learning contribution to fit ICLR, or when weighing ICLR's deep-learning center of gravity against a better-matched venue.4---56# ICLR Topic Selection78Use this when a project is still movable. ICLR is broad, but the paper should teach the learning9community something about representations, objectives, models, data, optimization, evaluation, or10deployment.1112## Strong ICLR signals1314- A clear representation-learning, model-behavior, optimization, generative modeling, RL, theory, or15 evaluation contribution.16- Evidence that changes how researchers should build, analyze, or judge learning systems.17- A simple central claim that can be verified by focused theory, experiments, or artifacts.18- Interest beyond one dataset, product, or application vertical.19- Honest limitations and ethics treatment for high-impact model or data claims.2021## Weak ICLR signals2223- Pure application paper with little learning insight.24- Incremental benchmark bump without mechanism, analysis, or robust evidence.25- Closed system claim that reviewers cannot inspect or reproduce.26- Dataset-only paper without a learning-representation or evaluation advance.27- Theory result disconnected from modern learning practice and not routed to a theory-focused venue.2829## Routing logic3031- Prefer NeurIPS or ICML for broader ML method/theory work with less ICLR-specific representation32 framing.33- Prefer AISTATS or UAI for statistics, uncertainty, causal, or probabilistic emphasis.34- Prefer ACL, CVPR, KDD, or robotics/HCI venues when the contribution is primarily domain-specific.35- Prefer workshops when the idea is timely but under-evidenced.3637## Fit-versus-route decision table3839ICLR's center of gravity is deep representation learning: architectures, self-supervision,40generative models, foundation models, RL with deep function approximation, optimization for deep41nets, interpretability, and alignment. Score the project against that center before routing.4243| Project shape | ICLR fit | Better route if not ICLR |44| --- | --- | --- |45| New self-supervised objective with analysis | Strong | — |46| Theory explaining a deep-net phenomenon | Strong | AISTATS/UAI if purely statistical |47| LLM/foundation-model behavior study | Strong | ACL if narrowly language-specific |48| Benchmark bump, no mechanism | Weak | Domain venue or workshop |49| Causal/uncertainty emphasis | Plausible | AISTATS or UAI |50| Deployed application, little learning insight | Weak | KDD, CVPR, robotics/HCI venue |5152## Worked vignette5354A team has a method that improves recommendation click-through in production. As written it is an55application paper. To make it ICLR-shaped, they extract the representation-learning claim: a new56contrastive objective that yields embeddings transferring across catalogs, demonstrated with an57ablation and a probe on a public dataset. The product result becomes one validation point, not the58contribution. If that reframing fails to surface a learning insight, the honest route is KDD.5960## Reviewer-pushback patterns6162- "No learning insight, just engineering." Reframe around the mechanism or route to a domain venue.63- "Dataset-only paper." Add an evaluation or representation advance, or target a datasets-and-64 benchmarks track instead.65- "Theory disconnected from practice." Tie the result to an observed deep-learning phenomenon.6667## Output format6869```text70[ICLR fit] strong / plausible / weak / no71[Core learning insight] <one sentence>72[Evidence required] <theory, experiment, benchmark, artifact>73[Best venue route] ICLR / NeurIPS / ICML / AISTATS / UAI / domain venue / workshop74[Reframe] <how to make the paper more ICLR-shaped>75```76
brycewang-stanford